Crime Mapping Analytics

There are important parallels between crime prevention and conflict prevention. About half-a-year ago I wrote a blog post on what crisis mapping might learn from crime mapping. My colleague Joe Bock from Notre Dame recently pointed me to an excellent example of crime mapping analytics.

The Philadelphia Police Department (PPD) has a Crime Analysis and Mapping Unit (CAMU) that integrates Geographic Information System (GIS) to improve crime analysis. The Unit was set up in 1997 and the GIS data includes a staggering 2.5 million new events per year. The data is coded from emergency distress calls and police reports and overlaid with other data such as bars and liquor stores, nightclubs, locations of surveillance cameras, etc.

For this blog post, I draw on the following two sources: (1) Theodore (2009). “Predictive Modeling Becomes a Crime-Fighting Asset,” Law Officer Journal, 5(2), February 2009; and (2) Avencia (2006). “Crime Spike Detector: Using Advanced GeoStatistics to Develop a Crime Early Warning System,” (Avencia White Paper, January 2006).

Introduction

Police track criminal events or ‘incidents’ which are “the basic informational currency of policing—crime prevention cannot take place if there is no knowledge of the location of crime.” Pin maps were traditionally used to represent this data.

GIS platforms now make new types of analysis possible beyond simply “eyeballing” patterns depicted by push pins. “Hot spot” (or “heat map”) analysis is one popular example in which the density of events is color coded to indicate high or low densities.

Hotspot analysis, however, in itself, does not tell people much they did not already know. Crime occurs in greater amounts in downtown areas and areas where there are more people. This is common sense. Police organize their operations around these facts already.

The City of Philadelphia recognized that traditional hot spot analysis was of limited value and therefore partnered with Avencia to develop and deploy a crime early warning system known as the Crime Spike Detector.

Crime Spike Detector

The Crime Spike Detector is an excellent example of a crime analysis analytics tool that serves as an early warning system for spikes in crime.

The Crime Spike Detector applies geographic statistical tools to discover abrupt changes in the geographic clusters of crime in the police incident database. The system isolates these aberrations into a cluster, or ‘crime spike’. When such a cluster is identified, a detailed report is automatically e-mailed to the district command staff responsible for the affected area, allowing them to examine the cluster and take action based on the new information.

The Spike Detector provides a more rapid and highly focused evaluation of current conditions in a police district than was previously possible. The system also looks at clusters that span district boundaries and alerts command staff on both sides of these arbitrary administrative lines, resulting in more effective deployment decisions.

More specfically, the spike detector analyzes changes in crime density over time and highlights where the change is statistically significant.

[The tool] does this in automated fashion by examining, on a nightly basis, millions of police incident records, identifying aberrations, and e-mailing appropriate police personnel. The results are viewed on a map, so exactly where these crime spikes are taking place are immediately understandable. The map supports ‘drill-through’ capabilities to show detailed graphs, tables, and actual incident reports of crime at that location.

Spike Detection Methodology

The Spike Detector compares the density of individual crime events over both space and time. To be sure, information is more actionable if it is geographically specified for a given time period regarding a specific type of crime. For example, a significant increase in drug related incidents in a specific neighborhood for a given day is more concrete and actable than simply observing a general increase in crime in Philadelphia.

The Spike Detector interface allows the user to specify three main parameters: (1) the type of crime under investigation; (2) the spatial and, (3) the temporal resolutions to analyze this incident type.

Obviously, doing this in just one way produces very limited information. So the Spike Detector enables end users to perform its operations on a number of different ways of breaking up time, space and crime type. Each one of these is referred to as a user defined search pattern.

To describe what a search pattern looks like, we first need to understand how the three parameters can be specified.

Space. The Spike Detector divides the city into circles of a given radius. As depicted below, the center points of these circles from a grid. Once the distance between these center points is specified, the radius of the circle is set such that the area of the circles completely covers the map. Thus a pattern contains a definition of the distance between the center points of circles.

Time. The temporal parameter is specified such that a recent period of criminal incidents can be compared to a previous period. By contrasting the densities in each circle across different time periods, any significant changes in density can be identified. Typically, the most recent month is compared to the previous year. This search pattern is know as bloc style comparison. A second search pattern is periodic, which “enables search patterns based on crime types that vary on a seasonal basis.”

Incident. Each crime is is assigned a Uniform Crime Reporting code. Taking all three parameters together, a search pattern might look like the following

“Robberies no Gun, 1800, 30, Block, 365″

This means the user is looking for robberies committed without a gun, with distance between cicle center points of 1,800 feet, over the past 30 days of crime data compared to the previous year’s worth of crime.

Determining Search Patterns

A good search pattern is determined by a combination of three factors: (1) crime type density; (2) short-term versus long-term patterns; and (3) trial and error. Crime type is typically the first and easiest parameter of the search pattern to be specified. Defining the spatial and temporal resolutions requires more thought.

The goal in dividing up time and space is to have enough incidents such that comparing a recent time period to a comparison time period is meaningful. If the time or space divisions are too small, ‘spikes’ are discovered which represent a single incident or few incidents.

The rule of thumb is to have an average of at least 4-6 crimes each in each circle area. More frequent crimes will permit smaller circle areas and shorter time periods, which highlights spikes more precisely in time and space.

Users are typically interested in shorter and most recent time periods as this is most useful to law enforcement while “though the longer time frames might be of interest to other user communities studying social change or criminology.” In any event,

Patterns need to be tested in practice to see if they are generating useful information. To facilitate this, several patterns can be set up looking at the same crime type with different time and space parameters. After some time, the most useful pattern will become apparent and the other patterns can be dispensed with.

Running Search Patterns

The spike detection algorithm uses simple statistical analysis to determine whether the probability that the number of recent crimes as compared to the comparison period crimes in a given circle area is possible due to chance alone. The user specifies the confidence level or sensitivity of the analysis. The number is generally set at 0.5% probability.

Each pattern results in a probability (or p-value) lattice assigned to every circle center point. The spike detector uses this lattice to construct the maps, graphs and reports that the spike detector presents to the user. A “Hypergeometic Distribution” is used to determine the p-values:

Where, for example:

N – total number of incidents in all Philadelphia for both the previous 365 days and the current 30 days.

G – total number of incidents in all Philadelphia for just the past 30 days.

n – number of incidents in just this circle for both the previous 365 days and the past 30 days.

x – number of incidents in just this circle for the past 30 days.

After the probability lattice is generated, the application displays spikes in order of severity and whether they have increased or decreased as compared to the previous day.

Conclusion

One important element of crisis mapping which is often overlooked is the relevance to monitoring and evaluation. With the Spike Detector, the Police Department “can assess the impact and effectiveness of anticrime strategies.” This will be the subject of a blog post in the near future.

For now, I conclude with the following comment from the Philadelphia Police Department:

GIS is changing the way we operate. All police personnel, from the police commissioner down to the officer in the patrol car, can use maps as part of their daily work. Our online mapping applications needed to be fast and user-friendly because police officers don’t have time to become computer experts. I think we’ve delivered on this goal, and it’s transforming what we do and how we serve the community.

Clearly, crime mapping analytics has a lot offer those of us interested in crisis mapping of violent conflict in places like the DRC and Zimbabwe. What we need is a Neogeography version of the Spike Detector.

6 responses to “Crime Mapping Analytics”

Thanks for the nice summary. The Crime Spike Detector work is now a few years old, and the project has developed since its origins that you outlined in your blog post. In 2007, Avencia was awarded a feasibility grant to create a more generalized version that we are now calling HunchLab. In 2008, we received a followup grant from NSF to turn HunchLab into an actual product. We are now mid-way through this process with an initial implementation for the City of Tacoma. We’ve made a number of improvements as well as made it a more generic system in terms of the type of data it can process and the sophistication of the alerting system, and we are looking forward to seeing it used in many more contexts.